Genome-wide association studies are a powerful approach for the identification of common alleles that contribute to disease susceptibility and phenotypic variation in humans. There are many existing collections of human pedigrees that are well characterized and suitable for gene-mapping studies. Cost-effective genome wide association studies often will start by genotyping a subset of individuals from these large collections. In this setting, we believe that powerful association tests will incorporate not only the phenotype and genotype data of the individuals selected for high-density genome-scanning but also the phenotypes and more limited genotype information that might be available on their relatives. [unreadable] [unreadable] We have assembled an interactive and innovative team of investigators with a proven track record in the development of methods for the analysis of gene-mapping data. We propose to develop and evaluate parametric and non-parametric methods for whole genome association mapping. Our methods will be able to incorporate genotype data generated by the International HapMap Project and other public resources to "fill in" missing genotypes in individual studies. In addition, we plan to develop methods that can integrate high-density SNP genotypes collected for a subset of individuals in a pedigree with phenotype and genotype data that may be available for their relatives. All the tools and methods we develop will be incorporated into publicly available software. [unreadable] [unreadable] We are funded to undertake a genome-wide association study for diabetes and related quantitative traits using samples from the Finland-United States Investigation of Non-Insulin Dependent Diabetes Mellitus (NIDDM, FUSION). This genome wide study will involve genotyping ~250,000 tagging SNPs in 800 cases and 800 controls, with additional follow-up genotyping of the most interesting SNPs in 2200 additional cases and 2200 additional controls. The resulting data, together with genotype data from at least two other genome-wide association scans, will allow us to evaluate and calibrate our methods for association mapping. [unreadable] [unreadable] [unreadable] [unreadable]